IBM Capstone Project - Identifying the best US city to host the next 'Laver Cup - 2020'¶

Introduction:¶

The "Laver Cup" is a Tennis Tournament organized each year, in the month of September, for a 3 days showdown of the world's top Tennis talents in the world, competing against one another. The "Laver Cup" pits six top European players against six of their counterparts from the rest of the World. One of the prime reasons to introduce the "Laver Cup" is to diversify, advocate and market the sport of Tennis to the entire world and also to lesser famous Tennis destinations of the world, where the sport is not a top event of the city. In a similar vein, the location of the "Laver Cup" rotates between Europe and rest of the world cities, each year. The "Laver Cup" was brought to life in 2017 and has been a mega-success since its inception. The Cup was inaugurated in Prague, Czech Republic in 2017, it then travelled to the US for the 1st time in 2018, in Chicago and back to Europe (Geneva, Switzerland), in 2019. In summary:

2017 - Prague, Czech Republic, Europe
2018 - Chicago, Illinois, US
2019 - Geneva, Switzerland, Europe

Business Problem:¶

The Laver Cup committee has been tasked to host the next Laver Cup, scheduled in September, 2020 in one of the major US cities. A number of major US cities entered the draw, out of which a total of 6 major US cities are shortliste, from the random draw. From the 6 major US cities, the Laver Cup committee must choose a city, from the final 6 cities, to host the Cup. The committee must ensure that the city will be ready from an infrastructure stand-point, to ensure the facilities in and around the main stadium are developed to support a flush of world audience. The committee must also ensure the neighborhood around the stadium is conducive to support a strong sports culture and captivate the Tennis audience for an ultimate fan experience.

Methodology:¶

To help the choose the next best US city to host "Laver Cup - 2020", the Tennis stadiums of the 4 major Grand Slams will be explored along with the 3 city centers which have hosted the Laver Cups, in the past. A master data set of all the 7 major stadiums (4 Grand Slam stadiums + 3 Laver Cup stadiums) will be created to cluster the stadiums and explore the similarity between the 7 stadiums.

Next, the 6 candidate city stadiums, in the US, will be explored in a dataframe (DF). The venues around these stadiums will be categorized and the frequency of the venue categories will be explored around each of the 6 city stadiums and compared against the 7 major world stadiums (found earlier).

To compare the 6 candidate US cities with the 7 major stadiums, a concatenated "Master Tennis Dataset" will be created which will include the frequency of venue categories against each of 13 major city stadiums (6 candidates + 7 historical stadiums).

ML model(s): A "KMeans" clustering algorithm will be utilized to segregate the 6 candidate city stadiums into different clusters. A pattern based algorithm was chosen here, since the dataset contains historical, static data on which the KMeans is known to perform with acceptable accuracy. In order to decide the number of clusters to solve each of the problems/ datasets, 2 optimization techniques were utilized:

The Elbow Method - This computes the "WSS" or 'within-sum-of-squares' error through finding the euclidian distance of each of the data points within a cluster to the center of other clusters. The point (number of clusters) where the "WSS" loss tends to flatten out (or create an elbow point), is the optimal number of clusters to use for the given dataset.
The Silhoutte Method - This method measures the similarity of an object within a cluster with other objects within the same cluster (cohesiveness) and compares it with the dissimilarity of the object with other clusters (separation). The Silhoutte score ranges from -1 to +1. A high value implies an appropriate clustering configuration, while lower values imply that number of clusters are too many or too few (i.e. the object in the cluster could be better classified compared to other clusters in the dataset). In the graph, the highest point (number of clusters with highest value), is considered as the optimal number of clusters.

Analysis:¶

After running the "KMeans" algorithm on the "Master Tennis Dataset", the candidate states / candidate cities (out of the 6), which are most similar to US Open and Chicago-Laver Cup event (i.e. if fall under the same cluster), were considered the best cities to host the next Laver Cup. Again, measurement metric utilized is 'Neighborhood Similarity'. The US Open was taken as the baseline as it is a considered a good attraction point for many sport enthusiasts and Tennis fans alike, being one of the 4 major Tennis destinations of world.

City Stadium exploration:¶

Of the candidate cities which fall under the same cluster as "US Open" and "Chicago-Laver-Cup", each of thsoe cities was explored on a map to visualize the sports, tourism, entertainment and food infrastructure around the stadium. This is significant to understand if the city os ready to host a world event within a 10 months time-period. Candidate cities, for which, the neighborhood was not well developed to support a huge inlfux of sports tourism, were discarded (e.g. Miami, FL was found to be under-developed in this respect and was discarded).

Final Analysis:¶

Of the candidate cities which were left, a final clustering algorithm was run, to find the city neighborhood which is closest to the US Open.

Data Source:¶

The Foursquare API was utilized to extract, load raw JSON, filter, structure into a DF and analyze datasets around the major Tennis stadiums of the world. For historical datasets, 7 major Tennis stadiums (4 Grand Slams stadiums + 3 Past Laver Cup stadiums) was explored using the 'search' criteria around the stadium locations using the Foursquare API (Account). The addresses used, for venues exploration, were as follows:

Australian Open --> 'Rod Laver Arena, Victoria, Australia'
US Open --> 'Flushing Meadows, Queens, NY'
Wimbledon --> 'Wimbledon, London, UK'
French Open --> '2 Avenue Gordon Bennett, Paris, France'
Laver Cup 2017 --> 'O2 Arena, Prague, Czech Republic'
Laver Cup 2018 --> 'United Center, Chicago, Illinois'
Laver Cup 2019 --> 'Palexpo, Geneva, Switzerland'

Similarly, the 'search' criteria was used for the 6 candidate cites, to explore the venues around each of the city stadiums. The addresses used were as follows:

Miami --> 'Crandon Park, Miami, FL'
Cincinatti --> 'Lindner Family Tennis Center, OH'
Palm Springs --> 'Indian Wells, Coachella Valley, CA'
Boston --> 'TD Garden, Boston, MA'
Arizona --> 'Arizona Veterans Memorial, AZ'
Dallas --> 'American Airlines Center, Victory Park, Dallas, TX'

Execution & Analysis:¶

Finding the 'latitude' and 'logitudes' of each of the 4 major Tennis stadiums and past 3 Laver Cup venues (7 Tennis stadiums):

Finding the latitude and longitude of all venues: 
[Latitude, Longitude]
 
French Open Location: 2 Avenue Gordon Bennett, Paris, France
48.8466913 2.2510958
 
Australian Open Location: Rod Laver Arena, Victoria, Australia
-37.821568799999994 144.978587525521
 
US Open, Location: USTA Billie Jean King National Tennis Center, Queens, NY
40.74957645 -73.84654175507845
 
Wimbledon, Location: Wimbledon, London, UK
51.4214787 -0.2064027
 
Laver Cup 1, Location: O2 Arena, Prague, Czech Republic
50.104754650000004 14.493621810111105
 
Laver Cup 2, Location: United Center, Chicago, Illinois
41.88068305 -87.67418510441388
 
Laver Cup 3, Location: Palexpo, Geneva, Switzerland
46.2332822 6.1184341

Setting up the client ID, Client Secret and Version of the API call:

Your credentails:
CLIENT_ID: 4U1D1Y4NBCWGRWG0AQ4OWDDCTYA13HNM3FRZBJIA3ES5ZN5Q
CLIENT_SECRET:FPLOCTRVGFFZ5Q4X5GFNPAOPZ4SCWPVHEG2QNDSKFZJ22QYE

Filtering the json file to store on the relevant data, converting it to a structured Pandas DF and exploring the columns of the DF:

Cleaning up the structured DF and cleaning column values, to include only the relevant columns:

Creating a master dataset of all 7 tennis stadiums by concatenating it into one large dataset:

Applying One-Hot-Encoding to the Dataframe, to categorize each of the venues into a column format:

(700, 214)

Aggregating the DF by 'Event_Venue' and finding the mean for each of the categories by 'Event_Venue'.

(7, 214)

Creating a new DF and applying the "get_common_categories" to obtain 10 most common venue categories for each of the 'Event_Venue':

Getting the top 25 venue categories for US Open as a comparison basis for other venue categories in other candidate cities:

Visualizing the map of US Open and exploring the venue categories around the US Open stadium at 'Flushing Meadows':¶

Running the 'Elbow-Method' and 'Silhoutte' to determine the optimal number of clusters to initiate for classification of 'Event Venue'.

[2 3 4 5 6] [0.15931380382831367, 0.11579927781493025, 0.07895378241672514, 0.07502260508320509, 0.05345227175645256]

Executing the 'KMeans' algorithm to label the 7 Tennis Venues into 2 clusters (2 clusters were chosen due to highest score at 2 clusters using the 'Silhouette' method):

array([1, 0, 0, 0, 0, 1, 0])

Inserting the 'Cluster Labels' column into the classification DF containing the 10 most common venues for each of the Tennis Stadiums:

Constructing the URL and storing venues and categories of venues around the 6 candidate US cities (listed below) in a raw JSON format:

Miami Location: 7300 Crandon Blvd, Miami, FL
25.70876075 -80.15998756670277
 
Cincinatti Location: Lindner Family Tennis Center, OH
39.34637095 -84.27727893436321
 
Palm Springs Open, Location: Indian Wells, Coachella Valley, CA
33.743764999999996 -116.22331524966393
 
Boston, Location: TD Garden, Boston, MA
42.36628265 -71.06221907010776
 
Arizona, Location: Arizona Veterans Memorial, AZ
35.0940824 -114.6341331
 
Dallas, Location: American Airlines Center, Victory Park, Dallas, TX
32.7905076 -96.81027213460834

Transforming the Raw JSON to a structured dataframe containing data columns:

Filtering out the DF to include relevant columns:

Cleaning up the column names:

Cleaining up the DF to include only unique state values in the 'state' column of the DF (for e.g. convert 'Arizona' to 'AZ' and 'Massachusetts' to 'MA'):

Further cleaning up the DF, excluding irrelevant 'state' values, aggregating the DF by 'state' and finding the mean number of categories for each of the 6 states / candidate cities

(6, 182)

Applying the "get_most_common_categories" function on the above DF to find 10 most common categories for each of the candidate stadium locations

Determing the optimal number of clusters to initate to classify the 6 candidate locations using 'Elbow Method' and 'Silhoutte Method':

[2 3 4 5] [0.12693762562607663, 0.14068242724701063, 0.10865375560964145, 0.07953041292334136]

Using the result of the 'Silhoutte Method' (number of clusters with highest score)', 3 clusters were chosen to execute the 'KMeans' algorithm:

array([2, 2, 0, 1, 0, 1])

Creating a 'master_tennis_set', a DF including a concatenation of all 7 Tennis venues along with 6 new candidate cities to host the Laver Cup:

Printing out the size of the concatenated DF:

(13, 281)

This implies that the above dataframe contains 13 unique Tennis event destinations which amount to an aggregate of 281 unique venue categories, among the 13 destinations. The 13 different Tennis event destinations are clustered against one another through computing the common venue categories shared between the different destinations (i.e. the venue categories similarity between any 2 given neighborhoods)

Cleaining the DF and replacing all 'NaN' with value of '0' to run the clustering algorithm:

Determing the optimal number of clusters to initate to classify the 6 candidate locations using 'Elbow Method' and 'Silhoutte Method':

[ 2  3  4  5  6  7  8  9 10 11] [0.11791793236653304, 0.12470947616876156, 0.12194144381406706, 0.11199609810981329, 0.09673824559027976, 0.07359790512081775, 0.056653659471948545, 0.061144107701112574, 0.050284327439997986, 0.03430166590086462]

Using the resulst of the 'Silhoutte Method (number of clusters with highest score)', 4 clusters were chosen to execute the 'KMeans' algorithm (Although the graph peaks at 3, there is a marginal drop in the Silhoutte score when cluster size varies from 3 to 4 clusters):

array([1, 2, 3, 0, 0, 1, 0, 0, 0, 2, 3, 2, 3])

Find the 10 most common venue categories for each of the tennis stadiums & labelling each of the 13 stadiums into different clusters:

master_set = master_tennis_most_common.sort_values(by = ['Cluster_Labels']).reset_index(drop=True)
master_set = master_set.drop(master_set.index[[0,1,2,5,7,10]]).reset_index(drop=True)

#Bubble Sort: 
temp = master_set.iloc[0].copy()
master_set.iloc[0] = master_set.iloc[2]
master_set.iloc[2] = temp
master_set

Utilizing 'Folium Map' to visualize the location of Miami stadium, in Florida, on a map:

Results:¶

After running the KMeans clustering algorithm on a list of 13 city venues, the city stadiums in the states of 'AZ - Arizona', 'MA - Massachusetts', 'CA- California', 'OH - Ohio' and 'FL-Florida' are found to differ, in neighborhood similarity from the Great stadiums of 'US Open', 'Wimbledon' and 'Chicago Laver Cup' tournament. After exploring each of the 5 shortlisted city destinations in the states of (AZ, MA, CA, FL, OH), it is found that Miami, FL currently does not yet have the sports infrastructure to host an event such as the Laver Cup, in the short-listed location. Since, the Laver Cup is scheduled to be hosted around last of September, 9 months may be too short of a time period for the Federation to fully equip the 'Crandon Tennis Center' with the facilities and environment conducive of creating an ultimate and world-class Tennis experience for the fans. As such, Miami, Florida has been taken off as a candidate city for 2020, however, it may be a hot candidate to choose when the Cup is back to the US, in 2022.

Palm Springs, CA; Boston, MA; Cincinatti, OH; Dallas, TX and Phoenix, AZ are the remaining venues from which Laver Cup Committee much choose to host the next Laver Cup.

NOTE: Since Palm Springs, CA and Cincinatti already host major ATP tournaments, the committee has decided to choose a "fresh venue" for this year's Laver Cup. This leaves the committee to choose between Boston, MA; Dallas, TX and Phoenix, AZ as the final 3 candidate cities to choose from.

To finally choose which one of the 3 cities is closer to U.S. Open in terms of neighborhood similarity of cultivating and inculcating a true Tennis fan experience, another clustering algorithm will be utilized to determine which of the 3 cities matches closer to the neighborhood of the famous 'Flushing Meadows' sports arena.

Displaying Top 10 most common venues between US Open Stadium, Dallas Stadium, Boston Stadium and Phoenix Stadium:

(11, 4)
(282, 4)

Comparing the top 25 most common venue categories around "US Open" with the category of venues found around stadiums of 'Phoenix', 'Boston' and 'Dallas'.

Visualizing the top 24 most common venue types found around Arthur Ashe Stadium.

Note: The most common venue type, 'Tennis Stadium' has been intentionally removed, as the stadaium(s) are not counted for neighborhood analysis purposes.

Visualizing the top 24 most common venue types found around Arthur Ashe Stadium as a 'HeatMap'

Filtering the DF to only include 'U.S. Open', 'AZ', 'TX' and 'MA':

Apply KMeans clustering to the "master_tennis_set_filtered" to find which of the 3 states / 3 candidate cities is closest to U.S. Open in terms of neighborhood similarity:

NOTE: Cluster size is chosen to be 2, since only 4 neighborhoods/destinations are being compared here and we're interested in finding cities which fall under the same cluster/category as U.S Open.

Applying 'PCA' to reduce the dimensionality of the 4 candidate cities into 3 features or dimensions. The cluster centers for the 4 candidate cities can be visualized in the below 3-D projected graph. As can be seen below, 'Phoenix' is identified to be the closest to 'US Open', as compared to 'Boston' and 'Dallas' in the 3-D cluster space.

Note: The 2 clusters can be identified in colors of blue and red, respectively.

Visualizing the neighborhood and type of venues around the chosen "Arizona Veterans Memorial Stadium" in Phoenix, AZ.¶

Map Center: 'Arizona Veterans Memorial Stadium'

Conclusion:¶

The Multi-sport stadium in Phoenix, AZ is one of the best locations for the respective Tennis Federation / Organization to host the next Laver Cup, to be hosted in 2020*. Since the neighborhood, in terms of the types of venues around the stadium, is most similar to that of the U.S. Open stadium in Flushing Meadows (both 'US Open' and 'AZ' state fall under the same cluster), there's expected to be a good turn-around of tennis fans to watch and support the intensive and celebratory 3-Days Laver Cup event, initiated by Roger Federer and Rod Laver.

On visualizing the neighborhood of Phoenix, AZ, we find a very similar city and lanscape to that of U.S. Open venue, around the 'USTA Billie Jean National Stadium'.

Moreover, both venues ('Arizona Veterans Memorial Stadium' in Phoenix and 'U.S. Open Stadiums' in NYC) have a high number of 'Sandwich places', 'Coffee Shops', 'American Restaurants' and 'Bars'. These venue types are very condusive of a celebratory environment and also attract inter-state and international trourists alike, especially if there's a large sporting event at the heart of it all.

*Update: The Laver Cup committee has chosen Boston, MA as the destination for Laver Cup-2020, which is a great option per the metrics analyzed as part of this Capstone Project. However, the Laver Cup committee may look into the option of hosting the next Laver Cup (hosted outside Europe), to be in Phoenix, AZ for 2022, scheduled from September 25th to 27th. Phoenix, AZ has the famous "Arizona Veterans Memorial Stadium" which is multi-purpose sports auditorium. The committee must look to strategise an expansion plan in and around the stadium to create an ultimate fan experience and bring world of Tennis to the hometown of Arizona. This will be a great opportunity to spread Tennis in southern parts of the US, such as Arizona and Texas, where Tennis is not yet the top 3 sports celebrated, in terms of viewer-ship and sponsorship.

	categories	hasPerk	id	location.address	location.cc	location.city	location.country	location.crossStreet	location.distance	location.formattedAddress	location.labeledLatLngs	location.lat	location.lng	location.postalCode	location.state	name	referralId	venuePage.id
0	[{'id': '4bf58dd8d48988d127941735', 'name': 'C...	False	4d6e383d30d5b1f7bcaec342	Palexpo	CH	Le Grand-Saconnex	Suisse	Route François-Peyrot 30	194	[Palexpo (Route François-Peyrot 30), 1218 Gran...	[{'label': 'display', 'lat': 46.23470047580338...	46.234700	6.119900	1218	Genève	Centre de presse de Palexpo	v-1582419989	NaN
1	[{'id': '4bf58dd8d48988d1ff931735', 'name': 'C...	False	4adcdab4f964a520855021e3	Route François-Peyrot 30	CH	Le Grand-Saconnex	Suisse	NaN	142	[Route François-Peyrot 30, 1218 Le Grand-Sacon...	[{'label': 'display', 'lat': 46.2343739420567,...	46.234374	6.119406	1218	Genève	Palexpo	v-1582419989	NaN
2	[{'id': '4bf58dd8d48988d1e2931735', 'name': 'A...	False	5888d0a5409f56237f153232	NaN	CH	NaN	Schweiz	NaN	76	[Schweiz]	[{'label': 'display', 'lat': 46.233673, 'lng':...	46.233673	6.119245	NaN	NaN	Art Genève	v-1582419989	NaN
3	[{'id': '4bf58dd8d48988d1fa931735', 'name': 'H...	False	5a8467821755625497c90bf4	Route Francois Peyrot 28	CH	Le Grand-Saconnex	Suisse	NaN	184	[Route Francois Peyrot 28, 1218 Le Grand Sacon...	[{'label': 'display', 'lat': 46.234896, 'lng':...	46.234896	6.117870	1218	Genève	ibis Budget Genève Palexpo Aéroport	v-1582419989	NaN
4	[{'id': '4bf58dd8d48988d147941735', 'name': 'D...	False	4f794bfce4b087957c5ca19d	NaN	CH	NaN	Schweiz	NaN	93	[Schweiz]	[{'label': 'display', 'lat': 46.234032545323, ...	46.234033	6.117885	NaN	NaN	Bar Restaurant Le Poivrier	v-1582419989	NaN

	Event	name	categories	lat	lng
0	French Open	Jardin des Serres d'Auteuil	Botanical Garden	48.846509	2.252549
1	French Open	Loge Orange	Stadium	48.846544	2.250335
2	French Open	Jardins de Roland Garros	Garden	48.847124	2.250180
3	French Open	Restaurant Le Roland Garros	French Restaurant	48.847226	2.250683
4	French Open	Court n°1	Tennis Court	48.846608	2.250470

	Event	name	categories	lat	lng
0	Wimbledon	Wimbledon Railway Station (WIM)	Train Station	51.421862	-0.205460
1	Wimbledon	Digby's	Coffee Shop	51.421402	-0.206698
2	Wimbledon	Wimbledon London Underground Station	Metro Station	51.421912	-0.205681
3	Wimbledon	Costa Coffee	Coffee Shop	51.421609	-0.206353
4	Wimbledon	Platform 5	Platform	51.421321	-0.206154

	Event	name	categories	lat	lng
95	Rod_Laver_Swizz	Banque Cantonale de Genève - Grand-Saconnex	Bank	46.233476	6.123379
96	Rod_Laver_Swizz	Consulat General de Turquie	Government Building	46.232052	6.115695
97	Rod_Laver_Swizz	Canicrok	Pet Store	46.230860	6.118221
98	Rod_Laver_Swizz	Bar Chez Tito	Restaurant	46.231095	6.118680
99	Rod_Laver_Swizz	la tour	None	46.230337	6.121895

	Event_Venue	Botanical Garden	French Restaurant	Garden	Stadium	Tennis Court
0	French Open	1	0	0	0	0
1	French Open	0	0	0	1	0
2	French Open	0	0	1	0	0
3	French Open	0	1	0	0	0
4	French Open	0	0	0	0	1

	Event_Venue	1st most common venue category	2nd most common venue category	3rd most common venue category	4th most common venue category	5th most common venue category	6th most common venue category	7th most common venue category	8th most common venue category	9th most common venue category	10th most common venue category
0	Aus Open	Event Space	Tennis Stadium	Warehouse	Tennis Court	General Entertainment	Café	Concert Hall	Food Service	Beer Garden	Office
1	French Open	Tennis Court	Stadium	Arts & Entertainment	French Restaurant	College Classroom	Event Space	Office	Sporting Goods Shop	Bus Line	Supermarket
2	Rod_Laver_Chicago	Bar	Stadium	General Entertainment	Outdoor Sculpture	Basketball Stadium	Sports Bar	Hockey Arena	Parking	Bus Line	Sporting Goods Shop
3	Rod_Laver_Czech	Lounge	Clothing Store	Sporting Goods Shop	Skating Rink	Shoe Store	Women's Store	Lingerie Store	Baby Store	Restaurant	Cosmetics Shop
4	Rod_Laver_Swizz	Convention Center	Hotel	Event Space	Exhibit	Café	Restaurant	Parking	Bank	Mediterranean Restaurant	Fair
5	US Open	Tennis Stadium	General Entertainment	Clothing Store	Tennis Court	Lounge	Sporting Goods Shop	Sandwich Place	Bar	Hot Dog Joint	Seafood Restaurant
6	Wimbledon	Platform	Coffee Shop	Bank	Fast Food Restaurant	Salon / Barbershop	Bus Stop	Grocery Store	Mobile Phone Shop	Bar	Shoe Repair

	Event_Venue	Latitude	Longitude	Cluster Labels	1st most common venue category	2nd most common venue category	3rd most common venue category	4th most common venue category	5th most common venue category	6th most common venue category	7th most common venue category	8th most common venue category	9th most common venue category	10th most common venue category	11th most common venue category	12th most common venue category	13th most common venue category	14th most common venue category	15th most common venue category	16th most common venue category	17th most common venue category	18th most common venue category	19th most common venue category	20th most common venue category	21st most common venue category	22nd most common venue category	23rd most common venue category	24th most common venue category	25th most common venue category
0	French Open	48.846691	2.251096	0	Tennis Court	Stadium	Arts & Entertainment	French Restaurant	College Classroom	Event Space	Office	Sporting Goods Shop	Bus Line	Supermarket	Sandwich Place	Building	Bus Stop	Restaurant	Tennis Stadium	Lounge	Gas Station	Garden	Other Great Outdoors	Shopping Plaza	Middle School	Soccer Stadium	Café	Real Estate Office	Rugby Stadium
1	US Open	40.749576	-73.846542	1	Tennis Stadium	General Entertainment	Clothing Store	Tennis Court	Lounge	Sporting Goods Shop	Sandwich Place	Bar	Hot Dog Joint	Seafood Restaurant	Restaurant	Cocktail Bar	Event Space	Ice Cream Shop	Wine Bar	Food Court	Office	Public Art	Sculpture Garden	Indian Restaurant	Chinese Restaurant	Salad Place	Italian Restaurant	Korean Restaurant	Fried Chicken Joint
2	Aus Open	-37.821569	144.978588	1	Event Space	Tennis Stadium	Warehouse	Tennis Court	General Entertainment	Café	Concert Hall	Food Service	Beer Garden	Office	Food & Drink Shop	Tea Room	Bar	Bridge	Coworking Space	Australian Restaurant	College Baseball Diamond	Park	Souvenir Shop	Pizza Place	Monument / Landmark	Plaza	Camera Store	Music Venue	Scenic Lookout
3	Wimbledon	51.421479	-0.206403	0	Platform	Coffee Shop	Bank	Fast Food Restaurant	Salon / Barbershop	Bus Stop	Grocery Store	Mobile Phone Shop	Bar	Shoe Repair	Cosmetics Shop	Office	Shoe Store	Sushi Restaurant	Snack Place	Spa	Stationery Store	Sandwich Place	Food Court	Building	Metro Station	Business Center	Furniture / Home Store	Clothing Store	Cocktail Bar
4	Rod_Laver_Czech	50.104755	14.493622	0	Lounge	Clothing Store	Sporting Goods Shop	Skating Rink	Shoe Store	Women's Store	Lingerie Store	Baby Store	Restaurant	Cosmetics Shop	Office	Meeting Room	Public Bathroom	General Entertainment	Jewelry Store	Kitchen Supply Store	Men's Store	Café	Candy Store	Casino	Hockey Arena	Cocktail Bar	Hockey Field	Indoor Play Area	Residential Building (Apartment / Condo)
5	Rod_Laver_Chicago	41.880683	-87.674185	0	Bar	Stadium	General Entertainment	Outdoor Sculpture	Basketball Stadium	Sports Bar	Hockey Arena	Parking	Bus Line	Sporting Goods Shop	Concert Hall	Music Venue	Restaurant	Bus Stop	Fried Chicken Joint	Circus	Vegetarian / Vegan Restaurant	Hot Dog Joint	Brewery	Office	Food Court	Indian Restaurant	Rock Club	Movie Theater	Food Truck
6	Rod_Laver_Swizz	46.233282	6.118434	0	Convention Center	Hotel	Event Space	Exhibit	Café	Restaurant	Parking	Bank	Mediterranean Restaurant	Fair	Historic Site	Gym	Lounge	Wine Shop	Pet Store	Art Gallery	Conference Room	Rental Car Location	Meeting Room	Bus Line	Bus Station	Sandwich Place	Fast Food Restaurant	Diner	Residential Building (Apartment / Condo)

	categories	hasPerk	id	location.address	location.cc	location.city	location.country	location.crossStreet	location.distance	location.formattedAddress	location.labeledLatLngs	location.lat	location.lng	location.postalCode	location.state	name	referralId	venuePage.id
0	[{'id': '5109983191d435c0d71c2bb1', 'name': 'T...	False	4dd6f027d22d38ef42dc3f07	6300 Kings Island Dr	US	Mason	United States	NaN	382	[6300 Kings Island Dr, Mason, OH 45040, United...	[{'label': 'display', 'lat': 39.3430659430824,...	39.343066	-84.276067	45040	OH	Kings Island T-Rex	v-1582420012	NaN
1	[{'id': '4e39a891bd410d7aed40cbc2', 'name': 'T...	False	57b4a7d7498e434792b6600a	5460 Courseview Drive	US	Mason	United States	NaN	439	[5460 Courseview Drive, Mason, OH 45220, Unite...	[{'label': 'display', 'lat': 39.350094, 'lng':...	39.350094	-84.275597	45220	OH	Western Southern Open	v-1582420012	NaN
2	[{'id': '4bf58dd8d48988d1e6941735', 'name': 'G...	False	4bb62b6e6edc76b0c380301c	6042 Fairway Dr	US	Mason	United States	NaN	431	[6042 Fairway Dr, Mason, OH 45040, United States]	[{'label': 'display', 'lat': 39.35017034332829...	39.350170	-84.278301	45040	OH	Kings Island Golf Course	v-1582420012	NaN
3	[{'id': '4f2a210c4b9023bd5841ed28', 'name': 'H...	False	500c7122e4b0ba42c29d1b9a	NaN	US	Mason	United States	NaN	320	[Mason, OH 45040, United States]	[{'label': 'display', 'lat': 39.34721740017701...	39.347217	-84.273723	45040	OH	Pine Run	v-1582420012	NaN
4	[{'id': '4bf58dd8d48988d182941735', 'name': 'T...	False	59b89e1db8fd9d24795639c1	NaN	US	Maineville	United States	NaN	240	[Maineville, OH 45039, United States]	[{'label': 'display', 'lat': 39.344765, 'lng':...	39.344765	-84.275410	45039	OH	Maineville, Oh	v-1582420012	NaN

	name	categories	city	state	lat	lng
0	Kings Island T-Rex	Theme Park Ride / Attraction	Mason	OH	39.343066	-84.276067
1	Western Southern Open	Tennis Stadium	Mason	OH	39.350094	-84.275597
2	Kings Island Golf Course	Golf Course	Mason	OH	39.350170	-84.278301
3	Pine Run	Housing Development	Mason	OH	39.347217	-84.273723
4	Maineville, Oh	Theme Park	Maineville	OH	39.344765	-84.275410

	name	categories	city	state	lat	lng
0	Paulaner Bar	Bar	Boston	MA	42.366235	-71.062060
1	TD Garden	Hockey Arena	Boston	MA	42.366329	-71.062153
2	MBTA North Station	Train Station	Boston	MA	42.366242	-71.062338
3	Track 9	Platform	Boston	MA	42.366979	-71.063192
4	Track 8	Platform	Boston	MA	42.367022	-71.063099

	state	ATM	Accessories Store	Advertising Agency	American Restaurant	Arcade	Art Gallery	Arts & Crafts Store	Athletics & Sports	Auditorium	Automotive Shop	BBQ Joint	Bakery	Bank	Bar	Baseball Field	Basketball Court	Basketball Stadium	Beach	Beer Garden	Big Box Store	Bike Shop	Boat or Ferry	Boutique	Buffet	Building	Burger Joint	Bus Line	Business Center	Business Service	Café	Capitol Building	Church	Clothing Store	Cocktail Bar	Coffee Shop	Comfort Food Restaurant	Concert Hall	Conference Room	Construction & Landscaping	Convenience Store	Cosmetics Shop	Coworking Space	Cuban Restaurant	Deli / Bodega	Dentist's Office	Department Store	Dessert Shop	Diner	Discount Store	Dive Bar	Doctor's Office	Dog Run	Donut Shop	EV Charging Station	Electronics Store	Event Space	Fabric Shop	Factory	Fast Food Restaurant	Field	Fire Station	Fish & Chips Shop	Food	Food & Drink Shop	Food Court	Food Service	Food Truck	Fried Chicken Joint	Gas Station	Gastropub	General Entertainment	General Travel	German Restaurant	Gift Shop	Golf Course	Government Building	Grocery Store	Gym	Gym / Fitness Center	Harbor / Marina	Hardware Store	High School	Historic Site	Hockey Arena	Hockey Field	Hospital	Hot Dog Joint	Hotel	Hotel Bar	Housing Development	Ice Cream Shop	Italian Restaurant	Juice Bar	Kids Store	Latin American Restaurant	Laundry Service	Light Rail Station	Liquor Store	Lounge	Market	Massage Studio	Mediterranean Restaurant	Mexican Restaurant	Middle School	Miscellaneous Shop	Mobile Phone Shop	Movie Theater	Moving Target	Museum	Music Venue	Nail Salon	Newsstand	Nightclub	Nightlife Spot	Non-Profit	Office	Other Great Outdoors	Other Nightlife	Park	Parking	Performing Arts Venue	Peruvian Restaurant	Pet Service	Pharmacy	Photography Lab	Pizza Place	Planetarium	Platform	Playground	Plaza	Police Station	Pool	Post Office	Preschool	Professional & Other Places	Radio Station	Residential Building (Apartment / Condo)	Restaurant	River	Rock Club	Roof Deck	Salon / Barbershop	Sandwich Place	School	Shopping Mall	Skate Park	Skating Rink	Snack Place	Soccer Field	Souvenir Shop	Spa	Speakeasy	Sporting Goods Shop	Sports Bar	Stadium	Storage Facility	Student Center	Supplement Shop	Surf Spot	TV Station	Taco Place	Tattoo Parlor	Tech Startup	Temple	Tennis Court	Tennis Stadium	Thai Restaurant	Theater	Theme Park	Theme Park Ride / Attraction	Thrift / Vintage Store	Toll Booth	Tourist Information Center	Trail	Train Station	Tram Station	University	Video Store	Water Park	Wine Bar	Women's Store
0	AZ	0.000000	0.00	0.00	0.020202	0.00	0.00	0.000000	0.00	0.010101	0.030303	0.000000	0.020202	0.010101	0.030303	0.020202	0.010101	0.00	0.00	0.010101	0.000000	0.00	0.00	0.00	0.000000	0.070707	0.010101	0.00	0.00	0.00	0.000000	0.010101	0.040404	0.00000	0.00	0.010101	0.00	0.000000	0.010101	0.00	0.040404	0.020202	0.000000	0.00	0.000000	0.020202	0.000000	0.000000	0.010101	0.010101	0.020202	0.000000	0.010101	0.000000	0.000000	0.000000	0.000000	0.010101	0.00	0.000000	0.010101	0.00	0.00	0.010101	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.010101	0.00	0.000000	0.00	0.010101	0.010101	0.020202	0.000000	0.010101	0.010101	0.010101	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.010101	0.020202	0.00	0.000000	0.00	0.010101	0.00	0.00	0.000000	0.000000	0.010101	0.00	0.030303	0.010101	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.010101	0.00	0.010101	0.000000	0.010101	0.010101	0.010101	0.000000	0.000000	0.010101	0.010101	0.000000	0.00	0.010101	0.010101	0.000000	0.000000	0.00	0.00	0.00	0.00	0.00	0.010101	0.00	0.000000	0.00	0.010101	0.010101	0.00	0.030303	0.010101	0.010101	0.000000	0.010101	0.00	0.000000	0.010101	0.00	0.000000	0.010101	0.000000	0.010101	0.000000	0.010101	0.000000	0.000000	0.00	0.00	0.010101	0.010101	0.00	0.010101	0.00	0.00	0.010101	0.00	0.00	0.00	0.010101	0.00	0.00	0.010101	0.000000	0.000000	0.000000	0.010101	0.00	0.000000	0.000000
1	CA	0.000000	0.00	0.00	0.000000	0.00	0.00	0.016393	0.00	0.000000	0.032787	0.000000	0.000000	0.032787	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.032787	0.00	0.00	0.00	0.000000	0.065574	0.000000	0.00	0.00	0.00	0.032787	0.000000	0.016393	0.04918	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.016393	0.032787	0.00	0.016393	0.000000	0.032787	0.000000	0.000000	0.016393	0.000000	0.032787	0.000000	0.016393	0.016393	0.016393	0.000000	0.000000	0.00	0.016393	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.016393	0.000000	0.00	0.000000	0.00	0.000000	0.016393	0.016393	0.032787	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.016393	0.000000	0.016393	0.00	0.00	0.016393	0.000000	0.00	0.032787	0.00	0.016393	0.00	0.00	0.000000	0.000000	0.000000	0.00	0.032787	0.000000	0.00	0.032787	0.00	0.000000	0.000000	0.000000	0.016393	0.000000	0.000000	0.00	0.000000	0.016393	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.032787	0.00	0.016393	0.000000	0.000000	0.000000	0.00	0.00	0.00	0.00	0.00	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.032787	0.000000	0.000000	0.016393	0.000000	0.00	0.000000	0.000000	0.00	0.016393	0.000000	0.032787	0.000000	0.000000	0.000000	0.000000	0.016393	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.00	0.00	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.016393	0.00	0.000000	0.016393
2	FL	0.000000	0.00	0.00	0.020000	0.01	0.01	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.00	0.12	0.000000	0.000000	0.01	0.03	0.01	0.000000	0.000000	0.010000	0.00	0.00	0.00	0.040000	0.000000	0.010000	0.00000	0.00	0.010000	0.00	0.000000	0.000000	0.01	0.010000	0.000000	0.000000	0.02	0.000000	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.01	0.01	0.000000	0.01	0.000000	0.000000	0.01	0.000000	0.03	0.00	0.010000	0.010000	0.00	0.000000	0.01	0.000000	0.000000	0.000000	0.010000	0.020000	0.000000	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.01	0.010000	0.00	0.01	0.000000	0.000000	0.000000	0.01	0.000000	0.000000	0.00	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.030000	0.030000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.00	0.01	0.01	0.000000	0.00	0.050000	0.02	0.000000	0.000000	0.00	0.010000	0.010000	0.000000	0.010000	0.000000	0.01	0.010000	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.010000	0.000000	0.02	0.00	0.000000	0.000000	0.01	0.000000	0.05	0.02	0.000000	0.00	0.00	0.01	0.000000	0.01	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000
3	MA	0.012048	0.00	0.00	0.000000	0.00	0.00	0.000000	0.00	0.012048	0.000000	0.012048	0.000000	0.000000	0.120482	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.012048	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.000000	0.000000	0.00000	0.00	0.000000	0.00	0.012048	0.000000	0.00	0.012048	0.012048	0.000000	0.00	0.000000	0.000000	0.000000	0.012048	0.000000	0.000000	0.012048	0.000000	0.000000	0.012048	0.000000	0.000000	0.024096	0.000000	0.00	0.000000	0.000000	0.00	0.00	0.000000	0.00	0.012048	0.012048	0.00	0.012048	0.00	0.00	0.060241	0.000000	0.00	0.012048	0.00	0.000000	0.012048	0.012048	0.000000	0.000000	0.000000	0.000000	0.00	0.072289	0.036145	0.000000	0.012048	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.000000	0.00	0.00	0.036145	0.012048	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.024096	0.012048	0.012048	0.000000	0.012048	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.012048	0.012048	0.000000	0.000000	0.000000	0.00	0.024096	0.000000	0.108434	0.012048	0.00	0.00	0.00	0.00	0.00	0.000000	0.00	0.012048	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.024096	0.000000	0.00	0.000000	0.000000	0.024096	0.000000	0.024096	0.000000	0.012048	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.000000	0.00	0.00	0.00	0.000000	0.00	0.00	0.000000	0.012048	0.012048	0.012048	0.000000	0.00	0.012048	0.000000
4	OH	0.000000	0.01	0.00	0.020000	0.01	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.010000	0.000000	0.00	0.01	0.00	0.020000	0.000000	0.000000	0.01000	0.02	0.010000	0.01	0.000000	0.010000	0.00	0.010000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.020000	0.000000	0.01	0.000000	0.000000	0.00	0.00	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.00	0.00	0.030000	0.020000	0.01	0.010000	0.03	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.02	0.010000	0.000000	0.01	0.000000	0.00	0.000000	0.00	0.00	0.010000	0.000000	0.000000	0.00	0.000000	0.010000	0.01	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.040000	0.000000	0.000000	0.010000	0.030000	0.000000	0.000000	0.000000	0.000000	0.03	0.010000	0.000000	0.000000	0.010000	0.00	0.01	0.01	0.00	0.00	0.000000	0.00	0.020000	0.00	0.000000	0.010000	0.00	0.010000	0.010000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.000000	0.00	0.000000	0.11	0.04	0.000000	0.01	0.07	0.05	0.000000	0.00	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.06	0.000000	0.000000
5	TX	0.000000	0.00	0.01	0.010000	0.00	0.03	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.090000	0.000000	0.020000	0.01	0.00	0.000000	0.000000	0.00	0.00	0.00	0.000000	0.020000	0.010000	0.01	0.00	0.01	0.000000	0.000000	0.000000	0.03000	0.02	0.000000	0.00	0.010000	0.010000	0.00	0.010000	0.000000	0.010000	0.00	0.000000	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.060000	0.000000	0.00	0.010000	0.000000	0.00	0.00	0.010000	0.00	0.000000	0.000000	0.01	0.000000	0.01	0.01	0.010000	0.000000	0.00	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.010000	0.000000	0.000000	0.000000	0.010000	0.01	0.00	0.000000	0.030000	0.01	0.000000	0.00	0.000000	0.01	0.00	0.070000	0.000000	0.000000	0.00	0.000000	0.000000	0.02	0.000000	0.00	0.000000	0.000000	0.010000	0.000000	0.000000	0.000000	0.01	0.000000	0.050000	0.010000	0.000000	0.000000	0.060000	0.000000	0.000000	0.000000	0.010000	0.00	0.010000	0.000000	0.000000	0.000000	0.02	0.00	0.00	0.00	0.00	0.000000	0.01	0.050000	0.01	0.000000	0.000000	0.01	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.00	0.000000	0.000000	0.020000	0.070000	0.010000	0.000000	0.000000	0.000000	0.00	0.01	0.000000	0.000000	0.01	0.000000	0.00	0.00	0.000000	0.00	0.00	0.00	0.000000	0.00	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000

	Event_Venue	1st most common category	2nd most common category	3rd most common category	4th most common category	5th most common category	6th most common category	7th most common category	8th most common category	9th most common category	10th most common category
3	AZ	Building	Convenience Store	Church	Mexican Restaurant	Bar	Salon / Barbershop	Automotive Shop	American Restaurant	Baseball Field	Bakery
6	US Open	Tennis Stadium	General Entertainment	Tennis Court	Clothing Store	Lounge	Bar	Sporting Goods Shop	Sandwich Place	Food Court	Hot Dog Joint
11	MA	Bar	Platform	Hockey Arena	General Entertainment	Hockey Field	Lounge	Pizza Place	Sporting Goods Shop	Moving Target	Event Space
12	TX	Bar	Sports Bar	Lounge	Event Space	Parking	Office	Residential Building (Apartment / Condo)	Italian Restaurant	Clothing Store	Art Gallery

	General Entertainment	Clothing Store	Tennis Court	Lounge	Sporting Goods Shop	Sandwich Place	Bar	Restaurant	Hot Dog Joint	Cocktail Bar	Seafood Restaurant	Ice Cream Shop	Wine Bar	Event Space	Food Court	Deli / Bodega	Food & Drink Shop	Salad Place	Cuban Restaurant	Sculpture Garden	Fountain	Creperie	Coffee Shop	Fast Food Restaurant
US Open	23.540000	10.70	10.7	8.560000	6.420000	6.420000	6.420000	4.28	4.280000	4.28	4.28	4.280000	4.280000	4.280000	4.280000	2.14	2.14	2.14	2.14	2.14	2.14	2.14	2.140000	2.14
Boston	10.963855	0.00	0.0	6.578313	4.385542	0.000000	21.927711	0.00	2.192771	0.00	0.00	0.000000	2.192771	4.385542	2.192771	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.000000	0.00
Phoenix	0.000000	0.00	0.0	0.000000	0.000000	1.838384	5.515152	0.00	0.000000	0.00	0.00	1.838384	0.000000	0.000000	0.000000	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.838384	0.00
Dallas	1.820000	5.46	0.0	12.740000	3.640000	0.000000	16.380000	1.82	0.000000	3.64	0.00	0.000000	0.000000	10.920000	0.000000	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.000000	1.82